Abstract
In intelligent retail environment, Ultra Wideband (UWB) is suitable for applications where the positioning accuracy is a critical parameter. This technology provides the use of several UWB antennas properly positioned inside a predetermined area and powered battery tags free to move inside the area. This has been used to deploy a Real Time Locating System (RTLS), which gives complete oversight of the customers and employees in the store and improves the customer experience. In this paper, it is described a tracking system based on UWB technology. The installation, in stores in Germany and Indonesia became the basis for a trajectory dataset and results presented in this paper based on a two-year experience that measured 10.4 million shoppers. Through the analysis of the collected tracking data, it allows to derive several information on the shoppers behaviour inside a store. Behaviours that concern flows of walking, most visited areas inside the space dedicated to the shopping and average travel times. The collection of this great quantity of data is very important for future marketing research that have the aim to attract people to purchase.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
www.nist.gov, last accessed: March 7, 2016.
References
Cheng, Q.J., Ng, J.K.Y., et al.: A wireless LAN location estimation system using center of gravity as an algorithm selector for enhancing location estimation. In: 2012 IEEE 26th International Conference on Advanced Information Networking and Applications (AINA), pp. 261–268. IEEE (2012)
Contigiani, M., Pietrini, R., Mancini, A., Zingaretti, P.: Implementation of a tracking system based on UWB technology in a retail environment. In: 2016 12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), pp. 1–6. IEEE (2016)
Farid, Z., Nordin, R., Ismail, M.: Recent advances in wireless indoor localization techniques and system. J. Comput. Netw. Commun. 2013 (2013)
Ferracuti, N., Norscini, C., Frontoni, E., Gabellini, P., Paolanti, M., Placidi, V.: A business application of rtls technology in intelligent retail environment: defining the shopper’s preferred path and its segmentation. J. Retail. Consum. Serv. 47, 184–194 (2019)
Frontoni, E., Mancini, A., Zingaretti, P.: Embedded vision sensor network for planogram maintenance in retail environments. Sensors 15(9), 21114–21133 (2015)
Frontoni, E., Raspa, P., Mancini, A., Zingaretti, P., Placidi, V.: Customers’ activity recognition in intelligent retail environments. In: Petrosino, A., Maddalena, L., Pala, P. (eds.) ICIAP 2013. LNCS, vol. 8158, pp. 509–516. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41190-8_55
Fuchs, C., Aschenbruck, N., Martini, P., Wieneke, M.: Indoor tracking for mission critical scenarios: a survey. Pervasive Mob. Comput. 7(1), 1–15 (2011)
Gezici, S., et al.: Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks. IEEE Signal Process. Mag. 22(4), 70–84 (2005)
Koyuncu, H., Yang, S.H.: A survey of indoor positioning and object locating systems. IJCSNS Int. J. Comput. Sci. Netw. Secur. 10(5), 121–128 (2010)
Lam, K.Y., Ng, J.K.Y., Wang, J., Ho Chuen Kam, C., Wai-Hung Tsang, N.: A pervasive promotion model for personalized promotion systems on using WLAN localization and NFC techniques. Mob. Inf. Syst. 2015 (2015)
Liciotti, D., Paolanti, M., Frontoni, E., Mancini, A., Zingaretti, P.: Person re-identification dataset with RGB-D camera in a top-view configuration. In: Nasrollahi, K., et al. (eds.) FFER/VAAM -2016. LNCS, vol. 10165, pp. 1–11. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56687-0_1
Liciotti, D., Paolanti, M., Frontoni, E., Zingaretti, P.: People detection and tracking from an RGB-D camera in top-view configuration: review of challenges and applications. In: Battiato, S., Farinella, G.M., Leo, M., Gallo, G. (eds.) ICIAP 2017. LNCS, vol. 10590, pp. 207–218. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70742-6_20
Liciotti, D., Paolanti, M., Pietrini, R., Frontoni, E., Zingaretti, P.: Convolutional networks for semantic heads segmentation using top-view depth data in crowded environment. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 1384–1389. IEEE (2018)
Liu, H., Darabi, H., Banerjee, P., Liu, J.: Survey of wireless indoor positioning techniques and systems. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 37(6), 1067–1080 (2007)
Mancini, A., Frontoni, E., Zingaretti, P., Placidi, V.: Smart vision system for shelf analysis in intelligent retail environments. In: ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. V004T08A045–V004T08A045. American Society of Mechanical Engineers (2013)
Marin-Hernandez, A., de Jesús Hoyos-Rivera, G., Garcia-Arroyo, M., Marin-Urias, L.F.: Conception and implementation of a supermarket shopping assistant system. In: 2012 11th Mexican International Conference on Artificial Intelligence (MICAI), pp. 26–31. IEEE (2012)
Newman, A.J., Foxall, G.R.: In-store customer behaviour in the fashion sector: some emerging methodological and theoretical directions. Int. J. Retail. Distrib. Manag. 31(11), 591–600 (2003)
Paolanti, M., Liciotti, D., Pietrini, R., Mancini, A., Frontoni, E.: Modelling and forecasting customer navigation in intelligent retail environments. J. Intell. Robot. Syst. 91(2), 165–180 (2018)
Paolanti, M., Romeo, L., Martini, M., Mancini, A., Frontoni, E., Zingaretti, P.: Robotic retail surveying by deep learning visual and textual data. Robot. Auton. Syst. 118, 179–188 (2019)
Paolanti, M., Sturari, M., Mancini, A., Zingaretti, P., Frontoni, E.: Mobile robot for retail surveying and inventory using visual and textual analysis of monocular pictures based on deep learning. In: 2017 European Conference on Mobile Robots (ECMR), pp. 1–6. IEEE (2017)
Yamabe, T., Lehdonvirta, V., Ito, H., Soma, H., Kimura, H., Nakajima, T.: Applying pervasive technologies to create economic incentives that alter consumer behavior. In: Proceedings of the 11th International Conference on Ubiquitous Computing, pp. 175–184. ACM (2009)
You, C.W., Wei, C.C., Chen, Y.L., Chu, H.H., Chen, M.S.: Using mobile phones to monitor shopping time at physical stores. IEEE Pervasive Comput. 2, 37–43 (2011)
Zeimpekis, V., Giaglis, G.M., Lekakos, G.: A taxonomy of indoor and outdoor positioning techniques for mobile location services. ACM SIGecom Exch. 3(4), 19–27 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Gabellini, P., D’Aloisio, M., Fabiani, M., Placidi, V. (2019). A Large Scale Trajectory Dataset for Shopper Behaviour Understanding. In: Cristani, M., Prati, A., Lanz, O., Messelodi, S., Sebe, N. (eds) New Trends in Image Analysis and Processing – ICIAP 2019. ICIAP 2019. Lecture Notes in Computer Science(), vol 11808. Springer, Cham. https://doi.org/10.1007/978-3-030-30754-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-30754-7_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30753-0
Online ISBN: 978-3-030-30754-7
eBook Packages: Computer ScienceComputer Science (R0)
